Intuit Payroll
From Intuit Payroll's careers page
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Our team is building the contextual knowledge stack: the foundation that captures, structures, and serves customer context so Intuit’s AI experiences understand each customer and act on it responsibly. Two capabilities sit at its core:
Knowledge authoring. This is how knowledge gets into the stack. We build the tools, schemas, and pipelines that let teams define, curate, validate, and version the knowledge AI agents rely on, including entities, relationships, domain concepts, business rules, and customer facts derived from activity. Authoring has to be easy for product and domain teams, rigorous enough to keep quality and lineage intact, and governed so every piece of knowledge has a clear owner, source, and access policy.
Context retrieval. This is how knowledge gets to the moment it’s needed. We build the retrieval layer that assembles the right context for an agent or experience in real time: finding relevant knowledge across structured stores, graphs, and vector or search indexes, ranking and filtering it, and honoring privacy and access controls. The result is context that is accurate, fresh, low-latency, and sized to fit within model context limits.
Build authoring services, APIs, and schemas for creating, updating, and versioning knowledge, with validation, review workflows, and change history
Develop pipelines that derive and enrich knowledge from customer events, documents, and product signals
Implement quality checks, provenance, and lineage so every knowledge item can be traced to its source
Apply ownership, consent, and fine-grained access controls at authoring time, not as an afterthought
Senior: Define the knowledge model and ontology strategy, set authoring standards across teams, and drive adoption by partner teams
Build low-latency retrieval services that combine structured lookups, graph traversal, keyword search, and semantic (vector) search
Implement ranking, filtering, freshness, and deduplication so agents receive concise, relevant context
Optimize for latency, cost, and scale, with caching, indexing, and observability for retrieval quality and performance
Build evaluation harnesses and metrics (relevance, coverage, latency, staleness) to measure and improve retrieval
Enforce permissions and privacy rules at query time
Senior: Own the retrieval architecture and tradeoffs across consistency, latency, and cost, and lead the evaluation strategy
BS/MS in Computer Science or a related field, or equivalent experience
5+ years of professional software engineering experience, including designing and operating production distributed systems
Strong programming skills in Java, Python, or Kotlin (or similar), with deep knowledge of data structures, algorithms, and distributed systems fundamentals
Experience with large-scale data and streaming technologies such as Kafka, Flink, Spark, Cassandra, Iceberg, or graph and search stores
Experience with cloud infrastructure (AWS preferred), CI/CD, and production operations
A record of leading projects from ambiguous requirements through launch, with measurable impact
Excellent written and verbal communication, and the ability to influence across teams
Nice to have
Hands-on experience with LLMs, RAG, vector or graph retrieval, or agent and context-engineering patterns
Background in data governance, lineage, or fine-grained access control
Experience with event-driven architectures, customer data platforms, or customer journey modeling
Experience mentoring engineers or acting as a tech lead
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
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